Sustainable energy sources, for example wind turbines, are gaining widespread popularity due to the increased energy demands and the desire to reduce the consumption of natural resources such as oil and gas. Modern wind farms are being deployed around the world to increase their overall energy output.
The wind turbines generally have one or more blades, typically located atop high towers, wherein the blades are rotated by the wind flow. The blades are coupled to a large diameter rotating shaft or rotor that is typically coupled to a generator via a gearbox (transmission) or by direct coupling.
The control technology tends to be complex, as the wind speeds fluctuate in intensity and direction. Horizontal wind shears and yaw misalignment, together with natural turbulence, are causes of asymmetric loads on the wind turbine hub and consequently the main shaft. These asymmetric loads together with those from vertical wind shears contribute to extreme loads and the increased number of fatigue cycles accumulated by a wind turbine system. These extreme loads and fatigue cycles can lead to failure of the system operations, inefficiencies, and possibly damage to the wind turbine components. The control becomes even more complex as the size of the wind turbines and energy output demands per unit increase.
There are asymmetric load control systems developed to measure bending moments acting on the hub and main shaft, and these systems serve as the primary control input for advanced controls. Certain asymmetric load control systems have been developed to reduce the effects associated with asymmetric loads. In a general sense, the asymmetric load control system receives measurement signals, wherein at least one operational parameter of the wind power plant such as the blade pitch, revolutions and/or yaw angle is adjusted depending on the measurement.
One of the difficulties for control processing is the ability to obtain highly accurate measurements. Another factor relates to the mean time between failures and the ability to obtain such accurate measurements for a satisfactory length of time. Another factor relates to the cost associated with implementation of the asymmetric load systems, as the industry demands a cost-effective approach that can be integrated with existing manufacturing. Measurement inaccuracies are particularly difficult to detect in unmanned wind power plants and can adversely impact fatigue and lifetime of the blades, drive train and tower.
One approach is to measure asymmetric loads by taking displacement measures on fixed elements of the turbine, however this has not provided the desired results. Another approach is to measure asymmetric loads by placing sensors on the blades. For example, one system deploys strain gauges mounted on the rotor blades for sensing the load on the rotor blades. This asymmetrical load sensing solution requires mounting sensors in the outer regions of the rotor blades that are difficult to access and are jeopardized by lightning. In addition, these strain gauges do not typically attain the required lifetime stress cycles. Another known technique uses optical fibers, such as the Fiber Bragg Grating Technology, wherein the intrinsic Bragg sensor elements are distributed along an optical fiber that is attached to the rotor blades. While the system is insensitive to lightning, the optical fibers are subject to damage and subsequent malfunction.
A different approach is based on measuring the deflection of the shaft of the wind turbine caused by the asymmetric loads. The asymmetric loads cause bending moments in the rotor blades that are then transferred to the rotating low-speed shaft, such that the asymmetrical loads appear as a force or bending moment component in the main shaft of the turbine The measurement of the asymmetric loads can thus be based on shaft measurements since the shaft deflects with respect to its non-loaded condition.
Existing measurement solutions are typically based on proximity sensors. Such sensing solutions require an extremely stiff reference (heavy support structure) and are vulnerable to bracket deflection and sensor drift as sources of error. Since the main shaft system is stiff, small offset errors in the sensor measurement, such as 0.1 mm, correspond to high errors in the bending moment evaluation, such as 200 kNm. Operation with offset errors in the sensor measurement can lead to more extreme loads and fatigue than operation with no advanced controls. Furthermore, manual removal of offset errors is not typically desirable. By way of example, in wind turbine applications, manual removal of offset errors would require maintenance to all turbines. Automatic calibration features can be applied to solve such problems, but sensor movement or drift of certain types cannot be excluded in-between calibrations without detection/faults. It is also possible for bedplate deformation or bearing moment reaction to be non-zero with thrust load relative to the gravity-only calibration state such that false signal offsets appear when operating.
The reliability issues with the measurement input for the asymmetric load control systems have not been adequately resolved and the difficulty is increasing with nominal power of the turbine. Conventional systems for measuring components of mechanical forces, such as torque or bending moments, in shafts have been based on the magnetostrictive effect of the shaft material or a ferromagnetic strip attached to the shaft. For example, certain magnetostrictive sensing solutions have been developed and applied in the automotive industry and can identify components of the mechanical forces in rotating shafts. Methods of improving the sensor performance by magnetizing this strip or the shaft are also known. However, the large dimensions of shafts, such as the wind turbine shaft, are not typically amenable to conventional encodings used on smaller diameter shafts. Other encoding technologies that employ magnetized strips to the shaft tend to have problems with accuracy over a long term.